There are not many easily available datasets with the high level of imbalance required for this work. However, a recent study [1]} has looked at data with IRs ranging from 9.08 to 970.6, with data made available online [2]}. It was also useful that these data were used in [1]} to compare HD-Ensemble, a high-performing hybrid, data-level ensemble classifier devised by that study's authors, to seven other state-of-the-art ensemble classifiers, including RUSBoost [4]}. The three most imbalanced datasets from [1]}, named there as skinnonskin, cod-rna, and ijcnn1 (IR = 970.6) were downloaded from [2]}.Modified, where necessary, to record target-1 for the minority class and target-0 for the majority, a requirement for the application of the ASTra activation function. In addition to the original files, the two largest datasets from [2]} were minority-undersampled to create more challenging problems (IRs of 3500, 4000, with one positive example in each of five folds), extracting five positive samples randomly at each run, to eliminate bias created by `good' or `bad' subset picks.
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